Progress towards an unassisted element identification from Laser Induced Breakdown Spectra with automatic ranking techniques inspired by text retrieval

In this communication, we will illustrate an algorithm for automatic element identification in LIBS spectra which takes inspiration from the vector space model applied to text retrieval techniques. The vector space model prescribes that text documents and text queries are represented as vectors of weighted terms (words). Document ranking, with respect to relevance to a query, is obtained by comparing the vectors representing the documents with the vector representing the query. In our case, we represent elements and samples as vectors of weighted peaks, obtained from their spectra. The likelihood of the presence of an element in a sample is computed by comparing the corresponding vectors of weighted peaks. The weight of a peak is proportional to its intensity and to the inverse of the number of peaks, in the database, in its wavelength neighboring. We suppose to have a database containing the peaks of all elements we want to recognize, where each peak is represented by a wavelength and it is associated with its expected relative intensity and the corresponding element. Detection of elements in a sample is obtained by ranking the elements according to the distance of the associated vectors from the vector representing the sample. The application of this approach to elements identification using LIBS spectra obtained from several kinds of metallic alloys will be also illustrated. The possible extension of this technique towards an algorithm for fully automated LIBS analysis will be discussed.